For array type column, explode() will convert it to n rows, where n is the number of elements in the array. For map/dictionary type column, explode() will convert it to nx2 shape, i.e., n rows, 2 columns (for key and value).

Explode for array#

CREATE TABLE IF NOT EXISTS my_table (name STRING, books ARRAY<STRING>, grades ARRAY<FLOAT>);
INSERT INTO my_table VALUES
("Tom", array('abc', 'efs'), array(85, 70, 91)),
('Alice', array('wer', 'mzdf'), array(70, 80, 90))

LATERAL VIEW explode will generate the different combinations of exploded columns. In the above case, column books has 2 elements, and column grades has 3 elements. So for each name, you now have 2x3=6 rows.

SELECT
  name, book_name, grade
FROM my_table
LATERAL VIEW explode(books) AS book_name
LATERAL VIEW explode(grades) AS grade

The generated table is like this:

Explode for map#

CREATE TABLE IF NOT EXISTS demo_table (name STRING, cnt MAP<STRING, INT>);
INSERT INTO demo_table VALUES
("ABC", map_from_entries(array(("k1", 1), ("k2", 2)))),
("BCD", map_from_entries(array(("k1", 3), ("K2", 5)))),
("YUV", map_from_entries(array(("k1", 9), ("k2", 8), ("k3", 7))))

The demo_table:

We can run LATERAL VIEW explode on the column cnt:

SELECT
  name, key, val
FROM nn_ecp.demo_table
LATERAL VIEW explode(cnt) AS key, val

The generated table is like this:

references#

• explode: https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.explode.html
• lateral view: https://spark.apache.org/docs/latest/sql-ref-syntax-qry-select-lateral-view.html
• map_from_entries() method: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/functions/map_from_entries
• explode() vs explode_outer() vs posexplode() vs posexplode_outer(): https://www.youtube.com/watch?v=ZIWdx204-0E